USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
- Speaker
- Yifei Wang
- Affiliation
- Ph.D. student, the University of Auckland
- Date
- Time
- – Asia/Shanghai
- Venue
- B1-518B, Research Building 4
Abstract
Today graph neural networks (GNN) are widely used for processing complex graph data. However, GNN models are vulnerable in real-world scenarios as the input graphs are prone to noises, potentially distorting node representations.
I will introduce our work on Structural Entropy-based Robust learning method for Graph Neural Network. We show that by minimizing the structural entropy, the affect of noises in input graphs can be alleviated.
Schedule
- 16:20-17:20 (Time in Beijing)
- 21:20-22:20 (Time in Auckland)
- April 29, 2022 (Friday)